vibe-coding-cn is a Chinese tutorial on using AI as a programming partner, covering prompts, reusable skills, workflows, context management, and software engineering practices. It guides developers from an initial idea through implementation and verification with AI coding tools such as Claude Code and Codex. The catalogue entries are the project’s documented skills, instruction, plugin, and command for applying this workflow.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add tradecatlabs/vibe-coding-cn --skill analyzing-malicious-pdf-with-peepdfgit clone --depth 1 https://github.com/tradecatlabs/vibe-coding-cnWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/tradecatlabs/vibe-coding-cn/analyzing-malicious-pdf-with-peepdf)<a href="https://agentmods.dev/skills/tradecatlabs/vibe-coding-cn/analyzing-malicious-pdf-with-peepdf"><img src="https://agentmods.dev/badge/skills/tradecatlabs/vibe-coding-cn/analyzing-malicious-pdf-with-peepdf/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/tradecatlabs/vibe-coding-cn/analyzing-malicious-pdf-with-peepdf"><img src="https://agentmods.dev/badge/skills/tradecatlabs/vibe-coding-cn/analyzing-malicious-pdf-with-peepdf.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00073 | $0.00803 |
| Opus 5 | $0.00036 | $0.00402 |
| Sonnet 5 | $0.00015 | $0.00161 |
| Haiku 4.5 | $0.00007 | $0.00080 |
Grade A, and why
analyzing-malicious-pdf-with-peepdf scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured today.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
100% identical to analyzing-malicious-pdf-with-peepdf — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 97 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Analyzing Malicious PDF with peepdf
When to Use
- When triaging suspicious PDF attachments from phishing emails
- During malware analysis of PDF-based exploit documents
- When extracting embedded JavaScript, shellcode, or executables from PDFs
- For forensic examination of weaponized document artifacts
- When building detection signatures for PDF-based threats
Prerequisites
- Python 3.8+ with peepdf-3 installed (pip install peepdf-3)
- pdfid.py and pdf-parser.py from Didier Stevens suite
- Isolated analysis environment (VM or sandbox)
- Optional: PyV8 for JavaScript emulation within peepdf
- Optional: Pylibemu for shellcode analysis
Workflow
- Triage with pdfid: Scan PDF for suspicious keywords (/JS, /JavaScript, /OpenAction, /Launch, /EmbeddedFile).
- Interactive Analysis: Open PDF in peepdf interactive mode to explore object structure.
- Identify Suspicious Objects: Locate objects containing JavaScript, streams, or encoded data.
- Extract Content: Dump suspicious streams and decode filters (FlateDecode, ASCIIHexDecode).
- Deobfuscate JavaScript: Analyze extracted JS for shellcode, heap sprays, or exploit code.
- Check VirusTotal: Use peepdf vtcheck to cross-reference file hash with AV detections.
- Generate IOCs: Extract URLs, domains, hashes, and shellcode signatures.
Key Concepts
| Concept | Description |
|---|---|
| /OpenAction | Automatic action executed when PDF is opened |
| /JavaScript /JS | Embedded JavaScript code in PDF objects |
| /Launch | Action that launches external applications |
| /EmbeddedFile | File embedded within the PDF structure |
| FlateDecode | zlib compression filter used to hide content |
| Object Streams | PDF objects stored in compressed streams |
Tools & Systems
| Tool | Purpose |
|---|---|
| peepdf / peepdf-3 | Interactive PDF analysis with JS emulation |
| pdfid.py | Quick triage scanning for suspicious keywords |
| pdf-parser.py | Deep object-level PDF parsing |
| VirusTotal | Hash lookup and AV detection cross-reference |
| CyberChef | Decode and transform extracted payloads |
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- today First seen · 97 lines · 73 tokens per session scan A 7cdd9a208444
analyzing-malicious-pdf-with-peepdf is a skill published in the GitHub repository tradecatlabs/vibe-coding-cn (16,183 stars, last pushed yesterday), licensed MIT. It adds 73 tokens to every session and 803 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to analyzing-malicious-pdf-with-peepdf, differing in 0 lines, and is treated as a copy.
Other skills, from other repositories
analyzing-malicious-pdf-with-peepdf
Perform static analysis of malicious PDF documents using peepdf, pdfid, and pdf-parser to extract embedded JavaScript, shellcode, and suspicious objects. Use when triaging a suspicious PDF attachment from a phishing email, analyzing a PDF-based exploit document, or building detection signatures for weaponized PDF…
analyzing-malicious-pdf-with-peepdf
A Chinese-language skill for examining suspicious PDF files with peepdf, pdfid, and pdf-parser. It is intended for static malware analysis, which studies a file without running it.
analyzing-malicious-pdf-with-peepdf
Perform static analysis of malicious PDF documents using peepdf, pdfid, and pdf-parser to extract embedded JavaScript, shellcode, and suspicious objects.
analyzing-malicious-pdf-with-peepdf
Perform static analysis of malicious PDF documents using peepdf, pdfid, and pdf-parser to extract embedded JavaScript, shellcode, and suspicious objects.
analyzing-malicious-pdf-with-peepdf
Perform static analysis of malicious PDF documents using peepdf, pdfid, and pdf-parser to extract embedded JavaScript, shellcode, and suspicious objects.
analyzing-malicious-pdf-with-peepdf
Perform static analysis of malicious PDF documents using peepdf, pdfid, and pdf-parser to extract embedded JavaScript, shellcode, and suspicious objects.